Estimating endpoint performance in unified communication systems

ABSTRACT

Performance of endpoints, client devices and servers within a communication system, is determined by collecting call quality data from each endpoint by a quality monitoring server/application. Call quality data includes predefined metrics based on network and end device characteristics during each call. Calls include voice, video, and data exchanges. Collected metric values are then aggregated according to a formula for consistency and scaled based on factors such as traffic volume. Resulting performance values are used to order the endpoints such that those with degraded quality and prioritized based on factors like traffic volume can be attended to first.

BACKGROUND

Voiced telecommunication systems have evolved from telephones that wereoriginally connected directly together in pairs to trunked exchangesystems and from there to completely digital systems where communicationis facilitated through exchange of data packets over a number ofinterconnected networks. For example, Voice Over IP (VOIP) telephonyuses internet protocol over wired and wireless networks, which may bepublic, secure, or a combination of the two.

Additional communication modes such as video, instant messaging,application or data sharing have also proliferated in recent years withthe availability of diverse types of communication networks and devicescapable of taking advantage of various features of these networks. Somemore recent systems (e.g. unified communication systems) take advantageof capabilities of modern networks and computing devices bringingtogether different communication networks and providing until nowunavailable functionality such as combining various modes ofcommunication, user defined routing mechanisms, and so on. In suchsystems, a network of servers manages end devices capable of handling awide range of functionality and communication while facilitatingcommunications between the more modern unified communication networkdevices and other networks (e.g. PSTN, cellular, etc.).

In a system that supports multiple forms of communication, it isimportant for administrators to be aware of the quality of experiencedelivered to the users by the system. This enables the administrators toadjust the configuration of the system as needed when the qualitydeteriorates. Quality of experience may degrade generally due toproblems in the network(s) or problems in the computing devices throughwhich the media travels. If the problem happens to be on the machines,it is difficult for the administrators to narrow down and focus on theproblematic ones in a system with a large number of devices.Determination of the source of the problem requires a systematicapproach to find the worst performing device(s).

SUMMARY

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended asan aid in determining the scope of the claimed subject matter.

Embodiments are directed to determining a performance of endpoints in acommunication system quantitatively and systematically, taking intoaccount aspects of the system such as volume of traffic to enhance acapability of administrators to address quality issues promptly andefficiently for the overall communication system.

These and other features and advantages will be apparent from a readingof the following detailed description and a review of the associateddrawings. It is to be understood that both the foregoing generaldescription and the following detailed description are explanatory onlyand are not restrictive of aspects as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an example unified communicationssystem;

FIG. 2 illustrates a simplified unified communications system withcommunication quality information being collected by a qualitymonitoring server from endpoints within the system;

FIG. 3 is a conceptual diagram, illustrating collection of communicationquality information, aggregation of collected results, and computationof a worst performing endpoint list based on collected data according toembodiments;

FIG. 4 illustrates a networked environment where embodiments may beimplemented;

FIG. 5 is a block diagram of an example computing operating environment,where embodiments may be implemented; and

FIG. 6 illustrates a logic flow diagram for a process of estimatingendpoint performance based on collection of communication qualityinformation from the endpoints according to embodiments.

DETAILED DESCRIPTION

As briefly discussed above, a performance of endpoints in acommunication system may be determined quantitatively andsystematically, to enhance a capability of administrators to addressquality issues promptly and efficiently for the overall communicationsystem. In the following detailed description, references are made tothe accompanying drawings that form a part hereof, and in which areshown by way of illustrations specific embodiments or examples. Theseaspects may be combined, other aspects may be utilized, and structuralchanges may be made without departing from the spirit or scope of thepresent disclosure. The following detailed description is therefore notto be taken in a limiting sense, and the scope of the present inventionis defined by the appended claims and their equivalents.

While the embodiments will be described in the general context ofprogram modules that execute in conjunction with an application programthat runs on an operating system on a personal computer, those skilledin the art will recognize that aspects may also be implemented incombination with other program modules.

Generally, program modules include routines, programs, components, datastructures, and other types of structures that perform particular tasksor implement particular abstract data types. Moreover, those skilled inthe art will appreciate that embodiments may be practiced with othercomputer system configurations, including hand-held devices,multiprocessor systems, microprocessor-based or programmable consumerelectronics, minicomputers, mainframe computers, and the like.Embodiments may also be practiced in distributed computing environmentswhere tasks are performed by remote processing devices that are linkedthrough a communications network. In a distributed computingenvironment, program modules may be located in both local and remotememory storage devices.

Embodiments may be implemented as a computer process (method), acomputing system, or as an article of manufacture, such as a computerprogram product or computer readable media. The computer program productmay be a computer storage media readable by a computer system andencoding a computer program of instructions for executing a computerprocess. The computer program product may also be a propagated signal ona carrier readable by a computing system and encoding a computer programof instructions for executing a computer process.

While the term “call” is mainly used in examples throughout thisdocument as referring to voice communications, it is not so limited.“Call” may also be used in reference to video communications, conferencecommunications, instant messaging, and other forms of communicationdiscussed herein in conjunction with estimating endpoint performance.

Referring to FIG. 1, diagram 100 of an example unified communicationssystem is illustrated. As discussed above, a system that supportsmultiple forms of communication with a complex interconnection ofdifferent networks and a large number of computing devices (servers,endpoints, specialized devices, etc.) may experience degradation ofquality in the experience delivered to the users due to problems in thenetwork(s) or problems in the computing devices. Without a systematicand well defined approach, it may be difficult, if not impossible, todetermine problematic devices in a system with a large number ofcomputing devices. The situation may become more complicated by the factthat not all devices carry the same amount of traffic. For thosemachines that carry more traffic, more weight may need to be assigned,since they “contribute” more to the overall system performance.

Embodiments are directed to determining a performance of each endpointin a communication system quantitatively and systematically, taking intoaccount aspects of the system such as volume of traffic to enhance acapability of administrators to address quality issues promptly andefficiently for the overall communication system.

In a unified communication system such as the one shown in diagram 100,users may communicate via a variety of end devices (102, 104), which areclient devices of the UC system. Each client device may be capable ofexecuting one or more communication applications for voicecommunication, video communication, instant messaging, applicationsharing, data sharing, and the like. In addition to their advancedfunctionality, the end devices may also facilitate traditional phonecalls through an external connection such as through PBX 124 to a PublicSwitched Telephone Network (PSTN). End devices may include any type ofsmart phone, cellular phone, any computing device executing acommunication application, a smart automobile console, and advancedphone devices with additional functionality.

Unified Communication (UC) Network(s) 110 includes a number of serversperforming different tasks. For example, UC servers 114 may provideregistration, presence, and routing functionalities. Presencefunctionality enables the system to route calls to a user to anyone ofthe client devices assigned to the user based on default and/or user setpolicies. For example, if the user is not available through a regularphone, the call may be forwarded to the user's cellular phone, and ifthat is not answering a number of voicemail options may be utilized.Since the end devices can handle additional communication modes, UCservers 114 may provide access to these additional communication modes(e.g. instant messaging, video communication, etc.) through accessserver 112. Access server 112 resides in a perimeter network and enablesconnectivity through UC network(s) 110 with other users in one of theadditional communication modes.

Audio/Video (A/V) conferencing server 118 provides audio and/or videoconferencing capabilities by facilitating those over an internal orexternal network. Mediation server 116 mediates signaling and media toand from other types of networks such as a PSTN or a cellular network(e.g. calls through PBX 124 or from cellular phone 122). Mediationserver 116 may also act as a Session Initiation Protocol (SIP) useragent (e.g. Business-To-Business User Agent “B2BUA”).

Quality Monitoring Server (QMS) 115 is tasked with collectingcommunication data such as statistical data associated with quantitativeand qualitative aspects of communications from endpoints within thesystem. Endpoint is a general term referring to each end device as wellas any server that is directly involved with communications within UCN110. For example, mediation server 116 and A/V conferencing server 118are directly involved with the communication as nodes within thenetwork. Therefore, performance of these servers may affect quality ofcommunications (e.g. degradation due to delays in the servers), and thusthey are considered endpoints.

In a UC system, users may have one or more identities, which is notnecessarily limited to a phone number. The identity may take any formdepending on the integrated networks, such as a telephone number, aSession Initiation Protocol (SIP) Uniform Resource Identifier (URI), orany other identifier. While any protocol may be used in a UC system, SIPis a preferred method. End devices and servers may communicate with eachother via SIP (e.g. collection of communication quality information bythe QMS from each endpoint).

The SIP is an application-layer control (signaling) protocol forcreating, modifying, and terminating sessions with one or moreparticipants. It can be used to create two-party, multiparty, ormulticast sessions that include Internet telephone calls, multimediadistribution, and multimedia conferences. SIP is designed to beindependent of the underlying transport layer.

SIP clients use Transport Control Protocol (“TCP”) or User DatagramProtocol (“UDP”) to connect to SIP servers and other SIP endpoints. SIPis primarily used in setting up and tearing down voice or video calls.However, it can be used in any application where session initiation is arequirement. These include event subscription and notification, terminalmobility, and so on. Voice and/or video communications are typicallydone over separate session protocols, typically Real Time Protocol(“RTP”).

SIP is intended to provide a signaling and call setup protocol forIP-based communications that can support a superset of the callprocessing functions and features present in the PSTN. SIP by itselfdoes not define these features, however. Rather, its focus is call-setupand signaling. SIP is also designed to enable the building of suchfeatures in network elements known as proxy servers and user agents.These are features that permit familiar telephone-like operations:dialing a number, causing a phone to ring, hearing ring back tones or abusy signal.

While the example system is described with specific servers and SIPfeatures in this and following figures, many other components (e.g.servers, firewalls, data stores, etc.) and protocols may be employed inimplementing embodiments using the principles described herein.Functionality of the systems enabling estimation of endpoint performancemay also be distributed among the components of the systems differentlydepending on component capabilities and system configurations.

FIG. 2 illustrates a simplified unified communications system withcommunication quality information being collected by a qualitymonitoring server from endpoints within the system.

A number of metrics may be used in a communication system to determineperformance quantitatively and qualitatively. A majority of thesemetrics may be measured by the endpoints (e.g. end devices, servers,etc.) during each call and then transmitted to QMS 215 for storage,aggregation, and analysis. The metrics may be uniform (i.e. measured andcollected by all endpoints) or specific (e.g. metrics specific for videocommunication capable end devices, metrics for servers only, and thelike). While a system according to embodiments may utilize any metricsto estimate endpoint performance, example one are discussed herein.

Network 210 in diagram 200 includes in addition to access server 212, UCserver(s) 214, A/V conferencing server 218, and mediation server 216,Multipoint Conferencing Unit (MCU) 211. MCU 211 is employed forfacilitating conference calls, audio or video. Each of these servers aswell as end devices 202, 204, and 206 are endpoints or machines that canaffect communication quality. Thus, each machine is configured tocollect call quality data and report the data to QMS 215 at theconclusion of each call. Alternatively, the collected data may beaggregated at the endpoints and provided to QMS 215 upon request orperiodically.

The example metrics according to a preferred embodiment include:

-   -   (1) call failure,    -   (2) network delay,    -   (3) Network Mean Opinion Score “NMOS” (based on network jitter        and packet loss, assuming perfect source and receiver        conditions),    -   (4) Listen Mean Opinion Score “LMOS” (based on compression,        mixing, and decoding degradation, assuming perfect source and        network conditions),    -   (5) Send Mean Opinion Score “SMOS” (based on microphone quality,        may be measured or based on a library of microphone        characteristics),    -   (6) Conversation Mean Opinion Score “CMOS” (based on delay).

A system according to embodiments, not only collects these metrics, butaggregates them computes a worst endpoint list based on weightedaggregation of the scores, and provides the list to an administratorsuch that the administrator can focus on endpoints that need immediateattention. In a complex and big network, a large number of endpoints mayprovide quality information that is below a predefined threshold at onepoint in the operation. As mentioned previously, an endpoint with asmall number of calls (e.g. an end device that is used for one call aday) may be de-prioritized compared to an endpoint that is handling, andthereby affecting, a large number of calls. Thus, weighted aggregationof collected metrics is an important aspect of a QMS monitoringcommunications quality over the network. To aggregate the collectedmetrics and determine an endpoint needing most urgent attention, aformula based approach is used according to one embodiment. For eachendpoint a Bad Performance Index (BPE) is computed using equation (1)based on metrics collected from calls in a given time window. Anincreased BPE indicates worse performance by a particular endpointmoving it to a higher spot on the worst performing endpoints list andcatching attention from the administrator.

Thus, for an endpoint (ep), the BPE may be expressed as:

BPE(ep)=SNSF*(α*NPM+(1−α)*PM, where 0≦α≦1.0.   [1]

SNSF is Sample Number Scaling Factor that allows the endpoints with moresamples (calls) to get more attention by the administrator while anendpoint with BPE having a small number of calls is suppressed (placedlower in the worst performing endpoints list). Network Payload Metrics(NPM) represents a linear combination of network based metrics, whilePayload Metrics (PM) represents a combination of end device basedmetrics. α is a combinatory factor and can be selected between 0 and 1as indicated above. In an example implementation, α may be set to 0.5.B_(count) is the sensitivity of BPE to the number of call samples.

The SNSF, which is introduced in equation [1] to incorporate the effectof the number of samples (calls), is defined following a statisticalsigmoid curve and may be expressed as:

$\begin{matrix}{{{SNSF} = \frac{1}{1 + \text{?}}}{\text{?}\text{indicates text missing or illegible when filed}}} & \lbrack 2\rbrack \\{{{SNSF} = 1},{{otherwise}.}} & \lbrack 3\rbrack\end{matrix}$

The setting of the SNSF to 1, as shown in equation [3], when the samplenumber is too high (2*StatsGenerationMinSamples parameter) is intendedto prevent an arithmetic overflow in a database server due to largenumber of samples. B_(count) is the sensitivity of BPE to the number ofcall samples. num_(samples) is the number of samples, andStatsGenerationMinSamples is a predefined parameter indicating a minimumnumber of samples (calls) necessary for useful results.

As mentioned above, network based metrics or NPM in equation [1] may bedetermined as a linear combination of network based metrics: callfailure, network delay, and NMOS. NPM may be expressed as:

$\begin{matrix}{{{NPM} = \frac{\text{?} + \text{?}}{\text{?}}}{\text{?}\text{indicates text missing or illegible when filed}}} & \lbrack 4\rbrack\end{matrix}$

Thus, NPM is the sum of network degraded calls, calls with high delaydue to network, and failed calls, divided by the total number of calls.From equation [4], the NPM ranges between 0 and 3. The measure may alsobe normalized to a specific range (e.g. 0 to 1) or used as is.

A PM metric may be presented by a normal distribution with (mean “μ”,standard deviation “σ”). According to one embodiment, three PM metricsmay be used to measure call quality based on end device performance:LMOS(μ_(L), σ_(L)), SMOS(μ_(S), σ_(S)), and CMOS(μ_(C), σ_(C)), asdiscussed above. In order to incorporate both quality and consistencyinto the performance estimate, PM may be defined as:

$\begin{matrix}{{PM} = {{\frac{1}{1 + \text{?}} \cdot \frac{\text{?} - \text{?}}{\text{?} - \text{?}}} + {\frac{1}{1 + \text{?}} \cdot \frac{\text{?} - \text{?}}{\text{?} - \text{?}}} + {\frac{1}{1 + \text{?}} \cdot {\frac{\text{?} - \text{?}}{\text{?} - \text{?}}.\text{?}\text{indicates text missing or illegible when filed}}}}} & \lbrack 5\rbrack\end{matrix}$

In equation [5], B_(x) is the growth rate of the sigmoid logisticfunction, and ω_(x) is where the highest growth rate exists. Basically,this logistic function maps the standard deviation, σ, which could rangefrom [0, infinite] into [0, 1] and promote or demote the values of bothends. If standard deviation value is large, it means the values are notconcentrated (not consistent) so the logistic function yields a highvalue, and it is multiplied to the distance between maximum MOS“MOS_(MAX)” and average MOS “avg(XMOS)”. Therefore, if MOS is away fromthe maximum MOS and has a high standard deviation, then PM becomeslarger. B_(x) and ω_(x) may be adjusted through iterative experiments.For statistical significance, a minimum number of samples may berequired when calculating PM. However, because of SNSF, endpoints withless than the minimum required samples may also be included. The SNSFdemotes the BPE of those endpoints.

The above described metrics, formulas for computing the metrics, andparameters are for example purposes and do not constitute a limitationon embodiments. Endpoint performance in a unified communication systemmay be determined and computed using any defined metric and anycomputation formula using the principles described herein by ensuringhigher traffic machines are more prominent and sample sizes arereasonable in the final analysis.

FIG. 3 is a conceptual diagram, illustrating collection of communicationquality information, aggregation of collected results, and computationof a worst performing endpoint list based on collected data according toembodiments.

Endpoints 330, which may include servers, end devices, and other devicesassociated with a unified communication network, each provide collecteddata 332 on communication quality to QMS server 315. The information maybe provided at the end of each call (voice or otherwise) or aggregatedand provided upon request or periodically. The information may beprovided directly to QMS 315 via a protocol such as SIP or through anintermediary device. For example, each endpoint may store theircollected information in a network data store and QMS 315 may retrievethe data from the data store later for analysis. The information may beprovided in any form such as simple SIP message, Extended MarkupLanguage (XML) data, and the like. Furthermore, each endpoint maycollect the same type of information (same metrics) or different metricsbased on their capabilities, and so on.

QMS 315 may maintain a list 334 of the endpoints and the metricsprovided by each endpoint. The list may be maintained in form of atable, a matrix, multi-dimensional data structure, and the like. Thelist 334 may identify each endpoint, collected metric types and valuesfrom each endpoint. Computation 336 is performed on the collected valuesof the quality metrics to determine performance of each endpoint andprioritize them according to which one performs worse and which oneshould be attended to first. These two attributes may be distinct, asdiscussed previously. Thus, computation 336 may take into account animportance of each endpoint based on a number of calls affected by thatendpoint such as weighting the calculation (e.g. SNSF discussed above).Traffic volume is not the only prioritization parameter. According toother embodiments, other considerations such as endpoints belonging to aspecial subnet may also be prioritized over others. For example, in anenterprise communication network, it may be desirable to attend toproblems within the administration subnet or customer service subnetfirst. Thus, metrics from endpoints belonging to those subnets may begiven higher weight (larger SNSF) in the computation.

As a result of computation 336, QMS 315 may generate a worst performingendpoint list 338, which orders the endpoints according to theirperformance such as their BPE. Because the worst performing endpointlist 338 is based on a scaled computation of performance factors such asNPM and PM, the highest endpoints on the list may be attended to firstassuming they are the most important ones to investigate. List 338 mayalso provide additional information such as details of each metric foreach endpoint, aggregations of categories of metric such as NPM and PM,and even historic information such as when the endpoint was servicedlast, etc. Part or all of the information included in list 338 may bestored in a data store for retrieval by another application, provideddirectly to a presentation, analysis, or scheduling application (forscheduling service). Alert(s) may be issued based on the performancevalues in the list 338. For example, if the BPE of an endpoint exceeds apredetermined threshold, an alert in form or a call, a voicemail, anemail, an instant message, etc. may be transmitted to an administrator.List 338 may also be provided textually or graphically through any userinterface to an administrator.

The operations and scenarios, as well as components of a unifiedcommunication system determining performance of endpoints, described inFIGS. 2 and 3 are exemplary for illustration purposes. A unifiedcommunication system according to embodiments may be implemented usingadditional or fewer components and other schemes using the principlesdescribed herein.

FIG. 4 is an example networked environment, where embodiments may beimplemented. Estimating endpoint performance as described previously maybe implemented locally or in a distributed manner over a number ofphysical and virtual clients and servers. Such a system may typicallyinvolve one or more networks such as PSTN 454, cellular network 4644,and UCN 410. At least one of the systems may be implemented inun-clustered systems or clustered systems employing a number of nodescommunicating over one or more networks.

A system according to embodiments may comprise any topology of servers,clients, Internet service providers, and communication media. Also, thesystem may have a static or dynamic topology. The term “client” mayrefer to a client application or a client device. A system according toembodiments may involve many more components, typical and relevant onesare discussed in conjunction with this figure.

Mediation server(s) 444 may provide signaling and media exchange betweenthe different systems. A PBX 452 and an RF modem 462 may be used forconnection between the PSTN and the cellular networks, respectively, andthe mediation server(s) 444. Client devices 441-443 communicate witheach other and with devices on other networks through UCN 410. The UCsystem may also include a UC server (not shown) for registering,routing, and other functionality.

QMS server 415 may monitor communication quality within the system bycollecting quantitative and qualitative call information from endpoints,aggregating the information by scaling it according to traffic volumeand/or other factors, and determining endpoint performance based on aformula combining different scaled metrics. Data associated with thesystem configuration (e.g. user names, phone numbers, call policies,configuration, records, etc.), metrics, metric values, and so on, may bestored in one or more data stores such as data stores 448, which may bedirectly accessed by the servers and/or clients of the system or managedthrough a database server 446. UCN 410 provides the backbone of the UCsystem and may employ a number of protocols such as SIP, RTP, and thelike. Client devices (e.g. 441-443) provide platforms for UCN userendpoints. Users may access the communication system using a clientdevice or one or more client applications running on a client device.

UCN 410 provides communication between the nodes described herein. Byway of example, and not limitation, UCN 410 may include wired media suchas a wired network or direct-wired connection, and wireless media suchas acoustic, RF, infrared and other wireless media.

Many other configurations of computing devices, applications, datasources, data distribution systems may be employed to implementestimation of endpoint performance. Furthermore, the networkedenvironments discussed in FIG. 4 are for illustration purposes only.Embodiments are not limited to the example applications, modules, orprocesses.

FIG. 5 and the associated discussion are intended to provide a brief,general description of a suitable computing environment in whichembodiments may be implemented. With reference to FIG. 5, a blockdiagram of an example computing operating environment is illustrated,such as computing device 500. In a basic configuration, the computingdevice 500 may be a server executing a communication quality monitoringapplication for addressing communication quality problems in a unifiedcommunication system. Computing device 500 may typically include atleast one processing unit 502 and system memory 504. Computing device500 may also include a plurality of processing units that cooperate inexecuting programs. Depending on the exact configuration and type ofcomputing device, the system memory 504 may be volatile (such as RAM),non-volatile (such as ROM, flash memory, etc.) or some combination ofthe two. System memory 504 typically includes an operating system 505suitable for controlling the operation of the computing device, such asthe WINDOWS® operating systems from MICROSOFT CORPORATION of Redmond,Wash. The system memory 504 may also include one or more softwareapplications such as program modules 506, other UC applications 522, andmonitoring application 524 with its computation module 526.

Other UC applications 522 may be separate applications or integralmodules of a hosted service application that provide advancedcommunication services through computing device 500 such signal routing,registration, and communication facilitation services with the enddevices of the UC system, as described previously. Monitoringapplication 524 collect data associated with endpoint performance fromthe endpoints such as the metrics described previously. The collecteddata is then used for computing a performance value for each endpointbased on the metric values as well as weight factors such as trafficvolume through the endpoint. An example of such a computation isprovided by the bad performance index BPE in conjunction with FIG. 2.The computation may be performed by monitoring application 524, thecomputation module 526 within the application, or by a separate module.This basic configuration is illustrated in FIG. 5 by those componentswithin dashed line 508.

The computing device 500 may have additional features or functionality.For example, the computing device 500 may also include additional datastorage devices (removable and/or non-removable) such as, for example,magnetic disks, optical disks, or tape. Such additional storage isillustrated in FIG. 5 by removable storage 509 and non-removable storage510. Computer storage media may include volatile and nonvolatile,removable and non-removable media implemented in any method ortechnology for storage of information, such as computer readableinstructions, data structures, program modules, or other data. Systemmemory 504, removable storage 509 and non-removable storage 510 are allexamples of computer storage media. Computer storage media includes, butis not limited to, RAM, ROM, EEPROM, flash memory or other memorytechnology, CD-ROM, digital versatile disks (DVD) or other opticalstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, or any other medium which can be used tostore the desired information and which can be accessed by computingdevice 500. Any such computer storage media may be part of device 500.Computing device 500 may also have input device(s) 512 such as keyboard,mouse, pen, voice input device, touch input device, etc. Outputdevice(s) 514 such as a display, speakers, printer, etc. may also beincluded. These devices are well known in the art and need not bediscussed at length here.

The computing device 500 may also contain communication connections 516that allow the device to communicate with other computing devices 518,such as over a wireless network in a distributed computing environment,for example, an intranet or the Internet. Other computing devices 518may include client devices and servers of the UC network defined asendpoints above. Communication connection 516 is one example ofcommunication media. Communication media may typically be embodied bycomputer readable instructions, data structures, program modules, orother data in a modulated data signal, such as a carrier wave or othertransport mechanism, and includes any information delivery media. Theterm “modulated data signal” means a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, communicationmedia includes wired media such as a wired network or direct-wiredconnection, and wireless media such as acoustic, RF, infrared and otherwireless media.

The claimed subject matter also includes methods. These methods can beimplemented in any number of ways, including the structures described inthis document. One such way is by machine operations, of devices of thetype described in this document.

Another optional way is for one or more of the individual operations ofthe methods to be performed in conjunction with one or more humanoperators performing some. These human operators need not be collocatedwith each other, but each can be only with a machine that performs aportion of the program.

FIG. 6 illustrates a logic flow diagram for process 600 of estimatingendpoint performance based on collection of communication qualityinformation from the endpoints according to embodiments. Process 600 maybe implemented in a quality monitoring server of a unified communicationsystem.

Process 600 begins with operation 602, where metric data associated withcommunication quality is collected from each endpoint. As mentionedpreviously, the data may be about any form of communication and may becollected at the end of each call, periodically, or upon request by thequality monitoring server. Processing advances from operation 602 tooptional operation 604.

At optional operation 604, the collected data is stored for subsequentretrieval, analysis, and the like. Processing continues to operation 606from optional operation 604, where a performance of each endpoint iscomputed based on the collected data and one or more weighting factorssuch as SNSF based on number of calls handled by the endpoint. Theperformance may be computed using any linear or non-linear combinationof the metrics conditioned for consistency or other aspects. Processingmoves from operation 606 to optional operation 608.

At optional operation 608, an alert is issued if an alert conditionbased on the computed performance is reached. For example, a specificBPE threshold may be defined for different types of endpoints. If thethreshold is reached, an alert may be transmitted to the administrator.Processing advances from optional operation 608 to operation 610.

At operation 610, a worst performing endpoint list is generated based onthe computed performances. The list simply orders the endpoints based ontheir scaled and computed performances such that the endpoints needingmost attention are listed at the top to draw the administrator'sattention. Processing moves from operation 610 to operation 612, wherethe list is provided to an administrator, another application, and so onfor display or further processing (e.g. analysis, storage). Afteroperation 612, processing moves to a calling process for furtheractions.

The operations included in process 600 are for illustration purposes.Estimation of endpoint performance in unified communication systems maybe implemented by similar processes with fewer or additional steps, aswell as in different order of operations using the principles describedherein.

The above specification, examples and data provide a completedescription of the manufacture and use of the composition of theembodiments. Although the subject matter has been described in languagespecific to structural features and/or methodological acts, it is to beunderstood that the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims and embodiments.

1. A method to be executed at least in part in a computing device forestimating endpoint performance in a unified communication network(UCN), the method comprising: collecting data associated with callmetrics; aggregating the collected data employing weighting based oncall quantity associated with each endpoint providing the data;generating a list of endpoints based on the aggregated data, wherein theendpoints are ordered in the list according to their performanceweighted based on the call quantity associated with each endpoint;providing the generated list to an administrator.
 2. The method of claim1, wherein the call quality metrics comprise: network based metricsincluding at least one from a set of: call failure, network delay, andNetwork Mean Opinion Score (NMOS) that is based on network jitter andpacket loss; and end device based metrics including at least one from aset of: Listen Mean Opinion Score (LMOS) that is based on compression,mixing, and decoding degradation; Send Mean Opinion Score (SMOS) that isbased on source microphone quality; and Conversation Mean Opinion Score(CMOS) that is based on conversation delay.
 3. The method of claim 2,wherein weighted performance of each endpoint is determined by computinga performance index based on a scaled linear combination of a networkbased metrics value and an end device based metrics value, and wherein ascaling factor is determined based on a number of calls handled by eachendpoint.
 4. The method of claim 3, wherein the network based metricsvalue is a combination of a number of network degraded calls, a numberof network delayed calls, and a number of failed calls divided by anumber of total calls.
 5. The method of claim 3, wherein the end devicebased metrics value is combined normal distributions of LMOS, SMOS, andCMOS.
 6. The method of claim 1, wherein the data is collected throughone of: transmission by each endpoint at conclusion of each call,periodic transmission of aggregated data for a plurality of calls byeach endpoint, and transmission of aggregated data for a plurality ofcalls by each endpoint upon request.
 7. The method of claim 1, whereinthe endpoints include one of: an end device for facilitatingcommunication through the UCN, an access server, a mediation server, anaudio/video conferencing server, and a multipoint conferencing unit. 8.The method of claim 1, wherein providing the list to the administratorincludes at least one from a set of: displaying the list on a userinterface, storing the list for subsequent retrieval, and providing thelist to one of an analysis application, a scheduling application, and apresentation application.
 9. The method of claim 1, wherein a callincludes one of: a voice call, a video call, an audio conference, avideo conference, an instant message session, an electronic mailexchange, an application sharing session, and a data sharing session.10. The method of claim 1, wherein the call quality metrics arecustomizable for each endpoint based on a capability of each endpoint.11. The method of claim 1, further comprising issuing an alert inresponse the aggregated data exceeding a predetermined threshold forendpoint performance.
 12. A system for estimating endpoint performancein a unified communication network (UCN), the system comprising: aplurality of endpoints comprising end devices, intermediary devices, andservers associated with the UCN for facilitating communications throughthe network, each endpoint configured to collect call quality metricsdata; a quality monitoring server (QMS) associated with the UCN, the QMSconfigured to: maintain a list of endpoints associated with the UCN andwith any external networks associated with the UCN; receive collectedcall quality metrics data from each endpoint; compute a scaled badperformance index (BPE) for each endpoint based on the collected callquality metrics data and scaled based on at least one of a number ofcalls handled by a respective endpoint and a location of the respectiveendpoint within the UCN; generate a worst performing endpoint list basedon ordering the plurality of endpoints according to their scaled BPEs;and issue an alert to an administrator if the scaled BPE of an endpointexceeds a predefined threshold.
 13. The system of claim 12, wherein thescaled BPE for each endpoint (ep) is computed by:

(

)=

·(

·

+(1−

)·

), where 0 ≦

≦1.0, a being a predefined combinatory factor, NPM being a combinationof network based metrics, PM being a combination of end device basedmetrics, and scaling factor SNSF being determined by:${{SNSF} = \frac{1}{1 + \text{?}}},{\text{?}\text{indicates text missing or illegible when filed}}$B_(count) being a sensitivity of BPE to a number of calls, num_(samples)being a number of calls, and StatGenerationMinSamples being a number ofminimum calls required for statistically significant computation of BPE.14. The system of claim 13, wherein SNSF is set to 1, if a number ofcalls is greater than a predetermined threshold based onStatGenerationMinSamples such that an overflow of a data store isavoided due to large amount of call quality data.
 15. The system ofclaim 13, wherein: NPM is a sum of a number of network degraded calls, anumber of network delayed calls, and a number of failed calls divided bya number of total calls; and PM defined by:${{PM} = {{\frac{1}{1 + \text{?}} \cdot \frac{\text{?} - \text{?}}{\text{?} - \text{?}}} + {\frac{1}{1 + \text{?}} \cdot \frac{\text{?} - \text{?}}{\text{?} - \text{?}}} + {\frac{1}{1 + \text{?}} \cdot \frac{\text{?} - \text{?}}{\text{?} - \text{?}}}}},{\text{?}\text{indicates text missing or illegible when filed}}$where B_(L), B_(S), B_(C) are growth rate of a sigmoid logisticfunction, ω_(L), ω_(S), ω_(C) are where highest growth rate exists,σ_(L), σ_(S), σ_(C), are standard deviations of respective mean opinionscores LMOS, SMOS, and CMOS, and MOS_(MAX) and MOS_(MIN) are maximum andminimum values for the mean opinion scores.
 16. The system of claim 12,wherein the QMS is further configured to maintain the list of endpointsand the worst performing endpoint list as one of: a table, a matrix, anda multi-dimensional data structure, and wherein the list of endpointsand the worst performing endpoint list include at least one from a setof: an endpoint identifier, a type of collected metric data, a value ofcollected metric data, a time of collection, and historic informationassociated with performance of each endpoint.
 17. The system of claim12, wherein the call quality metrics data is transmitted by theendpoints at one of: end of each call, expiration of a predefinedperiod, and receipt of a request from the QMS as one of a SIP messageand an Extended Markup Language document.
 18. A computer-readablestorage medium with instructions stored thereon for estimating endpointperformance in a unified communication network (UCN), the instructionscomprising: collecting data associated with call quality metrics fromendpoints comprising end devices, intermediary devices, and serversassociated with the UCN for facilitating communications through thenetwork at conclusion of each call, wherein a call includes at least onefrom a set of: a voice call, a video call, an audio conference, a videoconference, an instant message session, an electronic mail exchange, anapplication sharing session, and a data sharing session; computing a BPEfor each endpoint based on a scaled combination of network based metricvalue (NPM) and end device based metric value (PM), wherein NPM is a sumof a number of network degraded calls, a number of network delayedcalls, and a number of failed calls divided by a number of total calls,and PM is a combination of normal distributions of LMOS that is based oncompression, mixing, and decoding degradation, SMOS that is based onsource microphone quality, and CMOS that is based on conversation delay,taking into account a mean and a standard deviation of each Mean OpinionScore (MOS); ordering a list of endpoints based on the scaled BPE foreach endpoint such that endpoints with degraded performance and higherimportance compared to other endpoints are prioritized in the list;submitting the list to an application for at least one from a set of:storage, analysis, presentation, and scheduling of maintenance tasks.19. The computer-readable storage medium of claim 18, wherein a minimumnumber of calls is required for an endpoint prior to computing thescaled BPE for that endpoint.
 20. The computer-readable storage mediumof claim 18, wherein the instructions further comprise: normalizing theNPM and the PM prior to combining and scaling them to obtain the BPE.